import pandas as pd
import seaborn as sns
from scipy.stats import zscore
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as p
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, cut_tree
from ISLP.cluster import compute_linkage
from scipy.cluster.hierarchy import linkage

file_path = r"C:\Users\tiles\Downloads\causes-of-death-in-children-under-5.csv"
df = pd.read_csv(file_path)
df.head()

	Entity	Code	Year	Deaths - Invasive Non-typhoidal Salmonella (iNTS) - Sex: Both - Age: Under 5 (Number)	Deaths - Interpersonal violence - Sex: Both - Age: Under 5 (Number)	Deaths - Nutritional deficiencies - Sex: Both - Age: Under 5 (Number)	Deaths - Acute hepatitis - Sex: Both - Age: Under 5 (Number)	Deaths - Neoplasms - Sex: Both - Age: Under 5 (Number)	Deaths - Measles - Sex: Both - Age: Under 5 (Number)	Deaths - Digestive diseases - Sex: Both - Age: Under 5 (Number)	...	Deaths - Other neonatal disorders - Sex: Both - Age: Under 5 (Number)	Deaths - Whooping cough - Sex: Both - Age: Under 5 (Number)	Deaths - Diarrheal diseases - Sex: Both - Age: Under 5 (Number)	Deaths - Fire, heat, and hot substances - Sex: Both - Age: Under 5 (Number)	Deaths - Road injuries - Sex: Both - Age: Under 5 (Number)	Deaths - Tuberculosis - Sex: Both - Age: Under 5 (Number)	Deaths - HIV/AIDS - Sex: Both - Age: Under 5 (Number)	Deaths - Drowning - Sex: Both - Age: Under 5 (Number)	Deaths - Malaria - Sex: Both - Age: Under 5 (Number)	Deaths - Syphilis - Sex: Both - Age: Under 5 (Number)
0	Afghanistan	AFG	1990	48	105	1779	718	431	8649	477	...	7112	2455	3968	131	802	808	10	776	21	123
1	Afghanistan	AFG	1991	55	130	1822	741	439	8669	495	...	7574	2385	4650	129	781	800	12	748	41	132
2	Afghanistan	AFG	1992	68	155	2069	836	486	8539	554	...	8614	2370	5833	137	821	863	13	777	51	180
3	Afghanistan	AFG	1993	78	178	2427	970	549	8949	630	...	9458	2659	7800	155	923	979	16	872	24	239
4	Afghanistan	AFG	1994	83	194	2649	1063	589	10642	681	...	9823	3187	7894	170	1015	1064	19	961	52	259

5 rows × 32 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6840 entries, 0 to 6839
Data columns (total 32 columns):
 #   Column                                                                                                 Non-Null Count  Dtype 
---  ------                                                                                                 --------------  ----- 
 0   Entity                                                                                                 6840 non-null   object
 1   Code                                                                                                   6150 non-null   object
 2   Year                                                                                                   6840 non-null   int64 
 3   Deaths - Invasive Non-typhoidal Salmonella (iNTS) - Sex: Both - Age: Under 5 (Number)                  6840 non-null   int64 
 4   Deaths - Interpersonal violence - Sex: Both - Age: Under 5 (Number)                                    6840 non-null   int64 
 5   Deaths - Nutritional deficiencies - Sex: Both - Age: Under 5 (Number)                                  6840 non-null   int64 
 6   Deaths - Acute hepatitis - Sex: Both - Age: Under 5 (Number)                                           6840 non-null   int64 
 7   Deaths - Neoplasms - Sex: Both - Age: Under 5 (Number)                                                 6840 non-null   int64 
 8   Deaths - Measles - Sex: Both - Age: Under 5 (Number)                                                   6840 non-null   int64 
 9   Deaths - Digestive diseases - Sex: Both - Age: Under 5 (Number)                                        6840 non-null   int64 
 10  Deaths - Cirrhosis and other chronic liver diseases - Sex: Both - Age: Under 5 (Number)                6840 non-null   int64 
 11  Deaths - Chronic kidney disease - Sex: Both - Age: Under 5 (Number)                                    6840 non-null   int64 
 12  Deaths - Cardiovascular diseases - Sex: Both - Age: Under 5 (Number)                                   6840 non-null   int64 
 13  Deaths - Congenital birth defects - Sex: Both - Age: Under 5 (Number)                                  6840 non-null   int64 
 14  Deaths - Lower respiratory infections - Sex: Both - Age: Under 5 (Number)                              6840 non-null   int64 
 15  Deaths - Neonatal preterm birth - Sex: Both - Age: Under 5 (Number)                                    6840 non-null   int64 
 16  Deaths - Environmental heat and cold exposure - Sex: Both - Age: Under 5 (Number)                      6840 non-null   int64 
 17  Deaths - Neonatal sepsis and other neonatal infections - Sex: Both - Age: Under 5 (Number)             6840 non-null   int64 
 18  Deaths - Exposure to forces of nature - Sex: Both - Age: Under 5 (Number)                              6840 non-null   int64 
 19  Deaths - Diabetes mellitus - Sex: Both - Age: Under 5 (Number)                                         6840 non-null   int64 
 20  Deaths - Neonatal encephalopathy due to birth asphyxia and trauma - Sex: Both - Age: Under 5 (Number)  6840 non-null   int64 
 21  Deaths - Meningitis - Sex: Both - Age: Under 5 (Number)                                                6840 non-null   int64 
 22  Deaths - Other neonatal disorders - Sex: Both - Age: Under 5 (Number)                                  6840 non-null   int64 
 23  Deaths - Whooping cough - Sex: Both - Age: Under 5 (Number)                                            6840 non-null   int64 
 24  Deaths - Diarrheal diseases - Sex: Both - Age: Under 5 (Number)                                        6840 non-null   int64 
 25  Deaths - Fire, heat, and hot substances - Sex: Both - Age: Under 5 (Number)                            6840 non-null   int64 
 26  Deaths - Road injuries - Sex: Both - Age: Under 5 (Number)                                             6840 non-null   int64 
 27  Deaths - Tuberculosis - Sex: Both - Age: Under 5 (Number)                                              6840 non-null   int64 
 28  Deaths - HIV/AIDS - Sex: Both - Age: Under 5 (Number)                                                  6840 non-null   int64 
 29  Deaths - Drowning - Sex: Both - Age: Under 5 (Number)                                                  6840 non-null   int64 
 30  Deaths - Malaria - Sex: Both - Age: Under 5 (Number)                                                   6840 non-null   int64 
 31  Deaths - Syphilis - Sex: Both - Age: Under 5 (Number)                                                  6840 non-null   int64 
dtypes: int64(30), object(2)
memory usage: 1.7+ MB

df.describe()

	Year	Deaths - Invasive Non-typhoidal Salmonella (iNTS) - Sex: Both - Age: Under 5 (Number)	Deaths - Interpersonal violence - Sex: Both - Age: Under 5 (Number)	Deaths - Nutritional deficiencies - Sex: Both - Age: Under 5 (Number)	Deaths - Acute hepatitis - Sex: Both - Age: Under 5 (Number)	Deaths - Neoplasms - Sex: Both - Age: Under 5 (Number)	Deaths - Measles - Sex: Both - Age: Under 5 (Number)	Deaths - Digestive diseases - Sex: Both - Age: Under 5 (Number)	Deaths - Cirrhosis and other chronic liver diseases - Sex: Both - Age: Under 5 (Number)	Deaths - Chronic kidney disease - Sex: Both - Age: Under 5 (Number)	...	Deaths - Other neonatal disorders - Sex: Both - Age: Under 5 (Number)	Deaths - Whooping cough - Sex: Both - Age: Under 5 (Number)	Deaths - Diarrheal diseases - Sex: Both - Age: Under 5 (Number)	Deaths - Fire, heat, and hot substances - Sex: Both - Age: Under 5 (Number)	Deaths - Road injuries - Sex: Both - Age: Under 5 (Number)	Deaths - Tuberculosis - Sex: Both - Age: Under 5 (Number)	Deaths - HIV/AIDS - Sex: Both - Age: Under 5 (Number)	Deaths - Drowning - Sex: Both - Age: Under 5 (Number)	Deaths - Malaria - Sex: Both - Age: Under 5 (Number)	Deaths - Syphilis - Sex: Both - Age: Under 5 (Number)
count	6840.000000	6840.000000	6840.000000	6840.000000	6840.000000	6840.000000	6840.000000	6840.000000	6840.000000	6840.000000	...	6840.000000	6840.000000	6.840000e+03	6840.000000	6840.000000	6840.000000	6840.000000	6840.000000	6840.000000	6840.000000
mean	2004.500000	1041.740789	399.418567	6392.636842	826.106433	1472.511550	8527.408772	1235.561696	262.530409	304.512135	...	11726.084795	3876.111696	2.464864e+04	535.363743	1718.877047	2892.662719	3252.680702	2331.720468	12045.209064	2107.161111
std	8.656074	5943.506061	1549.064285	30815.191076	4399.035288	5794.139457	43502.336767	5006.538348	1119.050195	1208.144730	...	51612.890640	16817.425576	1.133132e+05	2156.814008	6934.211045	13333.898943	18169.939174	10832.408381	64858.902628	9180.674890
min	1990.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	1997.000000	0.000000	1.000000	1.000000	0.000000	7.000000	0.000000	2.000000	0.000000	1.000000	...	18.750000	0.000000	3.000000e+00	2.000000	4.000000	0.000000	0.000000	3.000000	0.000000	0.000000
50%	2004.500000	1.000000	10.000000	17.000000	2.000000	44.000000	1.000000	25.000000	4.000000	7.000000	...	178.000000	21.000000	9.800000e+01	15.000000	38.500000	11.000000	7.000000	27.000000	0.000000	12.000000
75%	2012.000000	42.000000	77.000000	903.250000	36.000000	283.000000	655.000000	290.250000	52.000000	78.000000	...	1773.250000	671.000000	3.822250e+03	142.000000	359.250000	388.000000	298.250000	291.250000	217.250000	277.250000
max	2019.000000	62334.000000	21223.000000	524103.000000	50184.000000	85197.000000	704288.000000	77952.000000	15916.000000	18047.000000	...	539952.000000	240021.000000	1.649581e+06	35583.000000	115624.000000	209562.000000	223680.000000	184096.000000	631523.000000	99248.000000

8 rows × 30 columns

df.isnull().sum()

Entity                                                                                                     0
Code                                                                                                     690
Year                                                                                                       0
Deaths - Invasive Non-typhoidal Salmonella (iNTS) - Sex: Both - Age: Under 5 (Number)                      0
Deaths - Interpersonal violence - Sex: Both - Age: Under 5 (Number)                                        0
Deaths - Nutritional deficiencies - Sex: Both - Age: Under 5 (Number)                                      0
Deaths - Acute hepatitis - Sex: Both - Age: Under 5 (Number)                                               0
Deaths - Neoplasms - Sex: Both - Age: Under 5 (Number)                                                     0
Deaths - Measles - Sex: Both - Age: Under 5 (Number)                                                       0
Deaths - Digestive diseases - Sex: Both - Age: Under 5 (Number)                                            0
Deaths - Cirrhosis and other chronic liver diseases - Sex: Both - Age: Under 5 (Number)                    0
Deaths - Chronic kidney disease - Sex: Both - Age: Under 5 (Number)                                        0
Deaths - Cardiovascular diseases - Sex: Both - Age: Under 5 (Number)                                       0
Deaths - Congenital birth defects - Sex: Both - Age: Under 5 (Number)                                      0
Deaths - Lower respiratory infections - Sex: Both - Age: Under 5 (Number)                                  0
Deaths - Neonatal preterm birth - Sex: Both - Age: Under 5 (Number)                                        0
Deaths - Environmental heat and cold exposure - Sex: Both - Age: Under 5 (Number)                          0
Deaths - Neonatal sepsis and other neonatal infections - Sex: Both - Age: Under 5 (Number)                 0
Deaths - Exposure to forces of nature - Sex: Both - Age: Under 5 (Number)                                  0
Deaths - Diabetes mellitus - Sex: Both - Age: Under 5 (Number)                                             0
Deaths - Neonatal encephalopathy due to birth asphyxia and trauma - Sex: Both - Age: Under 5 (Number)      0
Deaths - Meningitis - Sex: Both - Age: Under 5 (Number)                                                    0
Deaths - Other neonatal disorders - Sex: Both - Age: Under 5 (Number)                                      0
Deaths - Whooping cough - Sex: Both - Age: Under 5 (Number)                                                0
Deaths - Diarrheal diseases - Sex: Both - Age: Under 5 (Number)                                            0
Deaths - Fire, heat, and hot substances - Sex: Both - Age: Under 5 (Number)                                0
Deaths - Road injuries - Sex: Both - Age: Under 5 (Number)                                                 0
Deaths - Tuberculosis - Sex: Both - Age: Under 5 (Number)                                                  0
Deaths - HIV/AIDS - Sex: Both - Age: Under 5 (Number)                                                      0
Deaths - Drowning - Sex: Both - Age: Under 5 (Number)                                                      0
Deaths - Malaria - Sex: Both - Age: Under 5 (Number)                                                       0
Deaths - Syphilis - Sex: Both - Age: Under 5 (Number)                                                      0
dtype: int64

missing_code_entities_unique = df.loc[df['Code'].isnull(), 'Entity'].unique()
missing_code_entities_unique

array(['African Region (WHO)', 'East Asia & Pacific (WB)',
       'Eastern Mediterranean Region (WHO)', 'England',
       'Europe & Central Asia (WB)', 'European Region (WHO)', 'G20',
       'Latin America & Caribbean (WB)',
       'Middle East & North Africa (WB)', 'North America (WB)',
       'Northern Ireland', 'OECD Countries',
       'Region of the Americas (WHO)', 'Scotland', 'South Asia (WB)',
       'South-East Asia Region (WHO)', 'Sub-Saharan Africa (WB)', 'Wales',
       'Western Pacific Region (WHO)', 'World Bank High Income',
       'World Bank Low Income', 'World Bank Lower Middle Income',
       'World Bank Upper Middle Income'], dtype=object)

df = df.dropna()

indices_to_drop = df[df['Entity'] == 'World'].index
df = df.drop(indices_to_drop)

df

	Entity	Code	Year	Deaths - Invasive Non-typhoidal Salmonella (iNTS) - Sex: Both - Age: Under 5 (Number)	Deaths - Interpersonal violence - Sex: Both - Age: Under 5 (Number)	Deaths - Nutritional deficiencies - Sex: Both - Age: Under 5 (Number)	Deaths - Acute hepatitis - Sex: Both - Age: Under 5 (Number)	Deaths - Neoplasms - Sex: Both - Age: Under 5 (Number)	Deaths - Measles - Sex: Both - Age: Under 5 (Number)	Deaths - Digestive diseases - Sex: Both - Age: Under 5 (Number)	...	Deaths - Other neonatal disorders - Sex: Both - Age: Under 5 (Number)	Deaths - Whooping cough - Sex: Both - Age: Under 5 (Number)	Deaths - Diarrheal diseases - Sex: Both - Age: Under 5 (Number)	Deaths - Fire, heat, and hot substances - Sex: Both - Age: Under 5 (Number)	Deaths - Road injuries - Sex: Both - Age: Under 5 (Number)	Deaths - Tuberculosis - Sex: Both - Age: Under 5 (Number)	Deaths - HIV/AIDS - Sex: Both - Age: Under 5 (Number)	Deaths - Drowning - Sex: Both - Age: Under 5 (Number)	Deaths - Malaria - Sex: Both - Age: Under 5 (Number)	Deaths - Syphilis - Sex: Both - Age: Under 5 (Number)
0	Afghanistan	AFG	1990	48	105	1779	718	431	8649	477	...	7112	2455	3968	131	802	808	10	776	21	123
1	Afghanistan	AFG	1991	55	130	1822	741	439	8669	495	...	7574	2385	4650	129	781	800	12	748	41	132
2	Afghanistan	AFG	1992	68	155	2069	836	486	8539	554	...	8614	2370	5833	137	821	863	13	777	51	180
3	Afghanistan	AFG	1993	78	178	2427	970	549	8949	630	...	9458	2659	7800	155	923	979	16	872	24	239
4	Afghanistan	AFG	1994	83	194	2649	1063	589	10642	681	...	9823	3187	7894	170	1015	1064	19	961	52	259
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6835	Zimbabwe	ZWE	2015	106	31	1733	17	56	615	92	...	2269	518	1345	114	115	799	2178	126	1475	399
6836	Zimbabwe	ZWE	2016	112	32	1771	18	58	369	95	...	2249	559	1286	119	120	787	1827	133	1219	398
6837	Zimbabwe	ZWE	2017	111	32	1714	17	58	261	94	...	2245	544	1248	117	119	745	1658	133	1249	394
6838	Zimbabwe	ZWE	2018	109	31	1639	16	58	340	91	...	2203	568	1136	114	115	693	1458	129	1213	397
6839	Zimbabwe	ZWE	2019	108	31	1598	15	57	349	89	...	2190	536	1067	112	112	661	1394	127	1207	413

6120 rows × 32 columns

rename_dict = {
    'Deaths - Invasive Non-typhoidal Salmonella (iNTS) - Sex: Both - Age: Under 5 (Number)': 'INTS_Deaths',
    'Deaths - Interpersonal violence - Sex: Both - Age: Under 5 (Number)': 'Violence_Deaths',
    'Deaths - Nutritional deficiencies - Sex: Both - Age: Under 5 (Number)': 'Nutrition_Deaths',
    'Deaths - Acute hepatitis - Sex: Both - Age: Under 5 (Number)': 'Hepatitis_Deaths',
    'Deaths - Neoplasms - Sex: Both - Age: Under 5 (Number)': 'Neoplasms_Deaths',
    'Deaths - Measles - Sex: Both - Age: Under 5 (Number)': 'Measles_Deaths',
    'Deaths - Digestive diseases - Sex: Both - Age: Under 5 (Number)': 'Digestive_Deaths',
    'Deaths - Cirrhosis and other chronic liver diseases - Sex: Both - Age: Under 5 (Number)': 'Cirrhosis_Deaths',
    'Deaths - Chronic kidney disease - Sex: Both - Age: Under 5 (Number)': 'Kidney_Deaths',
    'Deaths - Cardiovascular diseases - Sex: Both - Age: Under 5 (Number)': 'Cardiovascular_Deaths',
    'Deaths - Congenital birth defects - Sex: Both - Age: Under 5 (Number)': 'Congenital_Deaths',
    'Deaths - Lower respiratory infections - Sex: Both - Age: Under 5 (Number)': 'Respiratory_Deaths',
    'Deaths - Neonatal preterm birth - Sex: Both - Age: Under 5 (Number)': 'Preterm_Deaths',
    'Deaths - Environmental heat and cold exposure - Sex: Both - Age: Under 5 (Number)': 'Heat_Cold_Deaths',
    'Deaths - Neonatal sepsis and other neonatal infections - Sex: Both - Age: Under 5 (Number)': 'Sepsis_Deaths',
    'Deaths - Exposure to forces of nature - Sex: Both - Age: Under 5 (Number)': 'Nature_Deaths',
    'Deaths - Diabetes mellitus - Sex: Both - Age: Under 5 (Number)': 'Diabetes_Deaths',
    'Deaths - Neonatal encephalopathy due to birth asphyxia and trauma - Sex: Both - Age: Under 5 (Number)': 'Encephalopathy_Deaths',
    'Deaths - Meningitis - Sex: Both - Age: Under 5 (Number)': 'Meningitis_Deaths',
    'Deaths - Other neonatal disorders - Sex: Both - Age: Under 5 (Number)': 'Other_Neonatal_Deaths',
    'Deaths - Whooping cough - Sex: Both - Age: Under 5 (Number)': 'Whooping_Cough_Deaths',
    'Deaths - Diarrheal diseases - Sex: Both - Age: Under 5 (Number)': 'Diarrheal_Deaths',
    'Deaths - Fire, heat, and hot substances - Sex: Both - Age: Under 5 (Number)': 'Fire_Heat_Deaths',
    'Deaths - Road injuries - Sex: Both - Age: Under 5 (Number)': 'Road_Deaths',
    'Deaths - Tuberculosis - Sex: Both - Age: Under 5 (Number)': 'Tuberculosis_Deaths',
    'Deaths - HIV/AIDS - Sex: Both - Age: Under 5 (Number)': 'HIV_AIDS_Deaths',
    'Deaths - Drowning - Sex: Both - Age: Under 5 (Number)': 'Drowning_Deaths',
    'Deaths - Malaria - Sex: Both - Age: Under 5 (Number)': 'Malaria_Deaths',
    'Deaths - Syphilis - Sex: Both - Age: Under 5 (Number)': 'Syphilis_Deaths'
}

df = df.rename(columns=rename_dict)
print(df.columns)

Index(['Entity', 'Code', 'Year', 'INTS_Deaths', 'Violence_Deaths',
       'Nutrition_Deaths', 'Hepatitis_Deaths', 'Neoplasms_Deaths',
       'Measles_Deaths', 'Digestive_Deaths', 'Cirrhosis_Deaths',
       'Kidney_Deaths', 'Cardiovascular_Deaths', 'Congenital_Deaths',
       'Respiratory_Deaths', 'Preterm_Deaths', 'Heat_Cold_Deaths',
       'Sepsis_Deaths', 'Nature_Deaths', 'Diabetes_Deaths',
       'Encephalopathy_Deaths', 'Meningitis_Deaths', 'Other_Neonatal_Deaths',
       'Whooping_Cough_Deaths', 'Diarrheal_Deaths', 'Fire_Heat_Deaths',
       'Road_Deaths', 'Tuberculosis_Deaths', 'HIV_AIDS_Deaths',
       'Drowning_Deaths', 'Malaria_Deaths', 'Syphilis_Deaths'],
      dtype='object')

cols = df.drop(columns=['Entity', 'Code', 'Year']).select_dtypes(include=np.number)
z_scores = cols.apply(zscore)
outlier_threshold = 3
outliers = (z_scores > outlier_threshold) | (z_scores < -outlier_threshold)
outlier_counts = outliers.sum()
print("Columns with outlier counts:")
print(outlier_counts[outlier_counts > 0])

Columns with outlier counts:
INTS_Deaths               38
Violence_Deaths           78
Nutrition_Deaths          55
Hepatitis_Deaths          33
Neoplasms_Deaths          70
Measles_Deaths            74
Digestive_Deaths         102
Cirrhosis_Deaths          78
Kidney_Deaths            141
Cardiovascular_Deaths     96
Congenital_Deaths         85
Respiratory_Deaths        73
Preterm_Deaths            58
Heat_Cold_Deaths          43
Sepsis_Deaths             75
Nature_Deaths             13
Diabetes_Deaths          177
Encephalopathy_Deaths    106
Meningitis_Deaths         97
Other_Neonatal_Deaths     48
Whooping_Cough_Deaths     83
Diarrheal_Deaths          57
Fire_Heat_Deaths          81
Road_Deaths              133
Tuberculosis_Deaths      125
HIV_AIDS_Deaths          165
Drowning_Deaths           68
Malaria_Deaths            82
Syphilis_Deaths          136
dtype: int64

filtered_data = df[(df['Year'] >= 1990) & (df['Year'] <= 2019)]
aggregated_data = filtered_data.groupby('Entity').sum()
aggregated_data = aggregated_data.drop(columns=['Year'])
aggregated_data

C:\Users\tiles\AppData\Local\Temp\ipykernel_24540\2972503105.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  aggregated_data = filtered_data.groupby('Entity').sum()

	INTS_Deaths	Violence_Deaths	Nutrition_Deaths	Hepatitis_Deaths	Neoplasms_Deaths	Measles_Deaths	Digestive_Deaths	Cirrhosis_Deaths	Kidney_Deaths	Cardiovascular_Deaths	...	Other_Neonatal_Deaths	Whooping_Cough_Deaths	Diarrheal_Deaths	Fire_Heat_Deaths	Road_Deaths	Tuberculosis_Deaths	HIV_AIDS_Deaths	Drowning_Deaths	Malaria_Deaths	Syphilis_Deaths
Entity
Afghanistan	4355	6307	58382	23437	17974	199342	20764	7046	8904	8038	...	322226	107240	236890	4528	26172	23411	1185	25157	3310	8450
Albania	0	85	457	13	798	196	1357	127	143	1073	...	7584	258	505	142	324	29	0	426	0	150
Algeria	422	392	3812	1110	4393	20391	5265	1884	2848	10669	...	78084	8150	26856	3659	26803	959	1158	5173	0	4200
American Samoa	0	0	17	0	0	33	0	0	0	0	...	46	22	4	0	0	0	0	0	0	55
Andorra	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Venezuela	0	2990	12437	129	4801	62	3363	439	716	1058	...	9828	686	38038	756	5376	755	1534	3369	523	432
Vietnam	928	1055	9295	667	11434	84565	10139	1777	2849	12965	...	27792	33658	38757	3021	9956	10835	2072	65895	1645	9328
Yemen	1924	1201	52547	6388	8405	100086	10464	3756	3108	19717	...	252675	32201	397881	7836	54648	5338	1759	12282	7081	10606
Zambia	1503	2487	68820	1015	13625	57746	8670	1752	2269	5779	...	59476	27087	230238	4001	8373	25753	216174	6805	124161	38809
Zimbabwe	2005	771	33936	334	1183	28844	2051	194	531	3908	...	62915	12635	47178	2267	2200	17075	236368	2297	56942	11729

204 rows × 29 columns

total_deaths_per_entity = df.groupby('Entity').sum().sum(axis=1)
top_50_countries = total_deaths_per_entity.sort_values(ascending=False).head(50)

colors = plt.cm.cool(np.linspace(0, 1, len(top_50_countries)))

plt.figure(figsize=(12, 8))
top_50_countries.plot(kind='bar', color=colors)
plt.title('Top 50 Countries by Total Number of Deaths')
plt.xlabel('Country')
plt.ylabel('Total Number of Deaths')
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()

C:\Users\tiles\AppData\Local\Temp\ipykernel_24540\11496973.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  total_deaths_per_entity = df.groupby('Entity').sum().sum(axis=1)

causes_of_death = df.drop(columns=['Entity', 'Code', 'Year']).sum()
top_20_causes = causes_of_death.sort_values(ascending=False).head(20)

colors = plt.cm.cool(np.linspace(0, 1, len(top_20_causes)))

plt.figure(figsize=(12, 8))
top_20_causes.plot(kind='bar', color=colors)
plt.title('Top 20 Causes of Death')
plt.xlabel('Cause of Death')
plt.ylabel('Total Number of Deaths')
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()

df_map = df.copy()

df_map['Total_Deaths'] = df_map.drop(columns=['Entity', 'Code', 'Year']).sum(axis=1)
yearly_deaths = df_map.groupby(['Entity', 'Year'])['Total_Deaths'].sum().reset_index()

fig = px.choropleth(yearly_deaths,
                    locations="Entity",
                    locationmode='country names',
                    color="Total_Deaths",
                    hover_name="Entity",
                    animation_frame="Year",
                    color_continuous_scale='Reds',
                    title="Total Deaths from 1990 to 2019")

fig.update_layout(
    geo=dict(showframe=False, showcoastlines=False, projection_type='equirectangular'),
    title=dict(x=0.5)
)

fig.show()

import matplotlib.pyplot as plt

df_cause = df.drop(columns=['Entity', 'Code'])
df_yearly = df_cause.groupby('Year').sum().reset_index()

if 'Total_Deaths' in df_yearly.columns:
    df_yearly = df_yearly.drop(columns='Total_Deaths')

num_causes = len(df_yearly.columns) - 1  
colors = plt.cm.tab10(np.linspace(0, 1, num_causes))

plt.figure(figsize=(12, 8))
for i, c in enumerate(df_yearly.columns[1:], start=1):
    plt.plot(df_yearly['Year'], df_yearly[c], label=c, color=colors[i % num_causes])

plt.title('Deaths by Disease from 1990 to 2019')
plt.xlabel('Year')
plt.ylabel('Number of Deaths')
plt.legend(loc='upper left', bbox_to_anchor=(1, 1), ncol=1)
plt.tight_layout()
plt.show()

Models

scaler = StandardScaler()
df_scaled = scaler.fit_transform(df.drop(columns=['Entity', 'Code', 'Year']))
pca = PCA()
pca_out = pca.fit_transform(df_scaled)

print(pd.DataFrame({'Center': scaler.mean_, 'Scale': scaler.scale_}, index=df.columns.drop(['Entity', 'Code', 'Year'])))

                            Center         Scale
INTS_Deaths             231.492320   1622.368214
Violence_Deaths          79.825817    303.457690
Nutrition_Deaths       1333.719118   6972.497881
Hepatitis_Deaths        160.559967   1616.560333
Neoplasms_Deaths        297.492974   1204.333079
Measles_Deaths         1814.687092   8421.696247
Digestive_Deaths        253.901797    938.604887
Cirrhosis_Deaths         54.085621    238.815144
Kidney_Deaths            63.699020    180.199557
Cardiovascular_Deaths   236.582190    898.611133
Congenital_Deaths      3175.634477  10591.726971
Respiratory_Deaths     6722.154902  30161.351947
Preterm_Deaths         4808.332026  20841.947352
Heat_Cold_Deaths         17.039706    123.145252
Sepsis_Deaths          1271.867157   4650.791920
Nature_Deaths            24.220098    505.365350
Diabetes_Deaths          16.040850     44.805750
Encephalopathy_Deaths  3647.752614  15066.581181
Meningitis_Deaths      1019.708007   4083.534715
Other_Neonatal_Deaths  2374.598366  14282.461832
Whooping_Cough_Deaths   807.175163   3630.187080
Diarrheal_Deaths       5213.969771  22619.833294
Fire_Heat_Deaths        111.063399    358.777053
Road_Deaths             352.145588   1244.571219
Tuberculosis_Deaths     611.118464   2500.691503
HIV_AIDS_Deaths         707.966013   2634.357974
Drowning_Deaths         469.680556   2781.121195
Malaria_Deaths         2654.809150  12169.034911
Syphilis_Deaths         448.185294   1475.692709

print("Number of Principal Components:", pca.n_components_)

Number of Principal Components: 29

components_df = pd.DataFrame(pca.components_.T, index=df.columns.drop(['Entity', 'Code', 'Year']), columns=[f'PC{i+1}' for i in range(pca.n_components_)])
print(components_df)

                            PC1       PC2       PC3       PC4       PC5  \
INTS_Deaths            0.094227 -0.279744  0.463189 -0.097988 -0.311449   
Violence_Deaths        0.192083  0.222668  0.146339 -0.055789 -0.035394   
Nutrition_Deaths       0.185846 -0.098552 -0.229165  0.030813  0.090948   
Hepatitis_Deaths       0.181262 -0.071792 -0.355795 -0.059059 -0.015808   
Neoplasms_Deaths       0.189168  0.308176  0.059227 -0.001661  0.086901   
Measles_Deaths         0.190922 -0.195315 -0.058128  0.001188 -0.004194   
Digestive_Deaths       0.204316  0.154755  0.155294 -0.032092 -0.091682   
Cirrhosis_Deaths       0.207761 -0.142255 -0.120225 -0.020686 -0.116311   
Kidney_Deaths          0.203901  0.060996  0.144088  0.068227 -0.049469   
Cardiovascular_Deaths  0.153002  0.310854  0.170639 -0.037460 -0.036617   
Congenital_Deaths      0.210769  0.182265  0.024624 -0.018932 -0.016313   
Respiratory_Deaths     0.221859  0.003205 -0.048193 -0.041515 -0.054279   
Preterm_Deaths         0.212775 -0.004188 -0.175389 -0.018307 -0.026728   
Heat_Cold_Deaths       0.201447  0.150567 -0.138287 -0.088340 -0.017715   
Sepsis_Deaths          0.204937 -0.181966 -0.100790  0.021038  0.033891   
Nature_Deaths          0.021530  0.025108 -0.033360  0.933804 -0.320722   
Diabetes_Deaths        0.151425  0.185858  0.211426  0.150184  0.291552   
Encephalopathy_Deaths  0.205653 -0.058167 -0.058967  0.010093  0.001525   
Meningitis_Deaths      0.200081 -0.191567  0.151517 -0.053601 -0.146360   
Other_Neonatal_Deaths  0.186166 -0.095823 -0.326120 -0.005729  0.037272   
Whooping_Cough_Deaths  0.216636 -0.095771 -0.121720 -0.004722  0.002849   
Diarrheal_Deaths       0.204768 -0.223387  0.033048 -0.042289 -0.123416   
Fire_Heat_Deaths       0.218313  0.060347  0.051362 -0.028369 -0.029486   
Road_Deaths            0.182034  0.251374  0.167682 -0.015125 -0.015366   
Tuberculosis_Deaths    0.205832 -0.118289 -0.014162  0.037863  0.044409   
HIV_AIDS_Deaths        0.067126 -0.195497  0.223282  0.218046  0.743423   
Drowning_Deaths        0.178385  0.319090 -0.047663  0.013700  0.006005   
Malaria_Deaths         0.129861 -0.318591  0.386044 -0.031780 -0.070264   
Syphilis_Deaths        0.193933 -0.157282 -0.001519  0.118707  0.260574   

                            PC6       PC7       PC8       PC9      PC10  ...  \
INTS_Deaths           -0.150491 -0.160460 -0.168650  0.019196  0.068765  ...   
Violence_Deaths       -0.211198 -0.073727 -0.162915 -0.251523  0.142197  ...   
Nutrition_Deaths       0.162417  0.419810 -0.199878 -0.001383  0.167245  ...   
Hepatitis_Deaths      -0.181882 -0.073030 -0.109682  0.225578  0.130812  ...   
Neoplasms_Deaths      -0.085367  0.004181  0.014093 -0.241094 -0.110874  ...   
Measles_Deaths         0.131766  0.354851  0.323575 -0.326737  0.191139  ...   
Digestive_Deaths      -0.102466 -0.013309 -0.087361 -0.031306 -0.249631  ...   
Cirrhosis_Deaths      -0.004083 -0.084571 -0.035769  0.175154 -0.093034  ...   
Kidney_Deaths          0.238568 -0.130608  0.174932 -0.108956 -0.115535  ...   
Cardiovascular_Deaths -0.069920  0.144762  0.567575  0.467288  0.356480  ...   
Congenital_Deaths     -0.090481 -0.163793 -0.011526  0.023456 -0.043899  ...   
Respiratory_Deaths    -0.076192  0.086761 -0.050587 -0.051147  0.123885  ...   
Preterm_Deaths        -0.084886 -0.221464  0.010628  0.092853 -0.044653  ...   
Heat_Cold_Deaths      -0.275361  0.125037 -0.239183  0.012007  0.136205  ...   
Sepsis_Deaths          0.034137 -0.287823 -0.004858  0.011011  0.111145  ...   
Nature_Deaths         -0.124072  0.033323 -0.017946  0.017272  0.030310  ...   
Diabetes_Deaths        0.524986 -0.306077 -0.212995  0.026004  0.468122  ...   
Encephalopathy_Deaths  0.027057 -0.265309  0.344594 -0.302319 -0.190070  ...   
Meningitis_Deaths     -0.045498 -0.004897  0.197662 -0.228545  0.005492  ...   
Other_Neonatal_Deaths -0.070486 -0.265279  0.060426  0.175507  0.013000  ...   
Whooping_Cough_Deaths -0.000840  0.023613 -0.063161  0.040207  0.011448  ...   
Diarrheal_Deaths      -0.003502  0.130329  0.078196 -0.001123  0.121607  ...   
Fire_Heat_Deaths      -0.076153  0.056770 -0.007674 -0.077277  0.032710  ...   
Road_Deaths            0.066890  0.188621  0.020785  0.382679 -0.417345  ...   
Tuberculosis_Deaths    0.300646  0.298881 -0.041852  0.027531 -0.235244  ...   
HIV_AIDS_Deaths       -0.472547  0.074565  0.070075 -0.014049 -0.019501  ...   
Drowning_Deaths        0.021078  0.156610 -0.273636 -0.205253 -0.059690  ...   
Malaria_Deaths         0.005307  0.110629 -0.258285  0.230162  0.052138  ...   
Syphilis_Deaths        0.234327 -0.102497 -0.021082  0.145453 -0.333198  ...   

                           PC20      PC21      PC22      PC23      PC24  \
INTS_Deaths            0.097433  0.001448 -0.074810 -0.094545 -0.097994   
Violence_Deaths       -0.173820 -0.033710 -0.050523  0.083480  0.103140   
Nutrition_Deaths      -0.127458 -0.044478  0.009286  0.094013 -0.186105   
Hepatitis_Deaths      -0.236619 -0.216256 -0.077713  0.303019  0.177520   
Neoplasms_Deaths      -0.092602  0.080114 -0.037015 -0.206970  0.142890   
Measles_Deaths         0.053259 -0.115713 -0.123097 -0.015506 -0.142276   
Digestive_Deaths      -0.055170 -0.379395  0.051284 -0.150414 -0.219953   
Cirrhosis_Deaths      -0.092435 -0.357556 -0.195848 -0.003489  0.239556   
Kidney_Deaths         -0.321186  0.343685 -0.127534 -0.012511 -0.199787   
Cardiovascular_Deaths  0.055162 -0.101249  0.098490 -0.050023  0.073617   
Congenital_Deaths     -0.166603  0.104662  0.302517 -0.228667  0.095590   
Respiratory_Deaths    -0.110788  0.086398 -0.053321 -0.144675 -0.227351   
Preterm_Deaths         0.108676 -0.137703  0.254089 -0.071234 -0.148213   
Heat_Cold_Deaths       0.035002  0.207158 -0.148331 -0.029819 -0.384965   
Sepsis_Deaths          0.647155  0.229967 -0.002104  0.026460 -0.036458   
Nature_Deaths          0.001507 -0.001788  0.001029  0.002632 -0.003212   
Diabetes_Deaths       -0.042636 -0.117215 -0.058150  0.066593 -0.010476   
Encephalopathy_Deaths  0.009330 -0.222256 -0.415252  0.056801  0.066756   
Meningitis_Deaths     -0.115091  0.092793  0.559466  0.541645 -0.002258   
Other_Neonatal_Deaths -0.085579  0.118892  0.098032 -0.148898 -0.244146   
Whooping_Cough_Deaths -0.121116  0.456805 -0.059503 -0.087762  0.564668   
Diarrheal_Deaths       0.047733  0.118182 -0.224383 -0.040080  0.085454   
Fire_Heat_Deaths       0.321166 -0.138664  0.057472 -0.110696  0.131770   
Road_Deaths            0.150643  0.216489 -0.267263  0.347573 -0.100635   
Tuberculosis_Deaths    0.081210 -0.069895  0.275319 -0.421664  0.135779   
HIV_AIDS_Deaths        0.004064 -0.001969 -0.019463  0.000040  0.015408   
Drowning_Deaths        0.311309 -0.091829  0.079973  0.269858  0.182744   
Malaria_Deaths        -0.119786 -0.071422 -0.039554 -0.015174  0.069500   
Syphilis_Deaths       -0.061982 -0.062689  0.100664  0.136883 -0.121650   

                           PC25      PC26      PC27      PC28      PC29  
INTS_Deaths           -0.088373  0.044640 -0.002440 -0.108274 -0.045844  
Violence_Deaths        0.057892  0.050430  0.056767  0.021887  0.028849  
Nutrition_Deaths       0.060651 -0.031770 -0.038811 -0.111907 -0.029652  
Hepatitis_Deaths      -0.400324 -0.138143 -0.279816 -0.366895  0.054902  
Neoplasms_Deaths      -0.388595  0.351079  0.316007 -0.234096  0.146334  
Measles_Deaths         0.086526  0.055086  0.052071  0.008244 -0.066508  
Digestive_Deaths       0.307334  0.024007 -0.262471 -0.292995 -0.095235  
Cirrhosis_Deaths      -0.147981  0.235191  0.326707  0.486375 -0.074721  
Kidney_Deaths         -0.228333 -0.123653  0.026343 -0.201089 -0.040781  
Cardiovascular_Deaths -0.002784 -0.016565  0.019231  0.005018 -0.027578  
Congenital_Deaths     -0.059441 -0.270984 -0.333200  0.332474 -0.250917  
Respiratory_Deaths     0.009130  0.014471 -0.242095  0.348853  0.754397  
Preterm_Deaths         0.220018 -0.408043  0.585638 -0.214154  0.257328  
Heat_Cold_Deaths      -0.105954 -0.203076  0.198300  0.237287 -0.404382  
Sepsis_Deaths         -0.236459 -0.000124 -0.138853 -0.011869  0.045211  
Nature_Deaths         -0.004240  0.002690 -0.001426  0.004114 -0.000293  
Diabetes_Deaths        0.077620  0.030078  0.022936  0.017752 -0.040112  
Encephalopathy_Deaths  0.131748 -0.218342 -0.136616  0.111867 -0.068003  
Meningitis_Deaths     -0.029798  0.122174  0.048276  0.108004 -0.035973  
Other_Neonatal_Deaths  0.265599  0.635336 -0.080919 -0.078857 -0.164563  
Whooping_Cough_Deaths  0.440254 -0.084923  0.022981 -0.074209  0.032438  
Diarrheal_Deaths       0.022970 -0.035097  0.047334 -0.171974 -0.151255  
Fire_Heat_Deaths      -0.004262  0.065565 -0.176340 -0.107244  0.071756  
Road_Deaths            0.066116  0.072285 -0.019305  0.017310  0.088178  
Tuberculosis_Deaths   -0.252701 -0.057748 -0.033566 -0.002233 -0.090820  
HIV_AIDS_Deaths       -0.001889 -0.011943 -0.000415  0.002573 -0.014906  
Drowning_Deaths        0.118622  0.073175 -0.012029  0.047145 -0.063032  
Malaria_Deaths         0.074032  0.002485  0.052472  0.004714  0.040699  
Syphilis_Deaths       -0.041984 -0.047687  0.012801  0.092254  0.028923  

[29 rows x 29 columns]

plt.figure(figsize=(14, 10))

for i in range(28): 
    plt.scatter(pca_out[:, i], pca_out[:, i + 1], label=f'PC{i+1} vs PC{i+2}', s=5)

plt.xlabel('Principal Component')
plt.ylabel('Principal Component')
plt.title('Scatter Plot of the First 29 Principal Components')
plt.legend()
plt.show()

print("Explained Variance:", pca.explained_variance_)

Explained Variance: [1.97196643e+01 2.45048380e+00 2.19999567e+00 1.00752567e+00
 9.58763219e-01 5.18799377e-01 4.43362442e-01 3.55857051e-01
 3.02347592e-01 2.21220670e-01 2.04948758e-01 1.25738712e-01
 1.04572661e-01 8.55214733e-02 5.88939451e-02 4.80105053e-02
 4.28070082e-02 3.57672799e-02 2.20169171e-02 2.13706380e-02
 1.66299190e-02 1.26423574e-02 1.15981840e-02 9.93009277e-03
 7.62041773e-03 5.67412152e-03 5.28917665e-03 4.89361606e-03
 2.79377675e-03]

print("Explained Variance Ratio:", pca.explained_variance_ratio_)

Explained Variance Ratio: [6.79877314e-01 8.44856343e-02 7.58495239e-02 3.47365877e-02
 3.30553985e-02 1.78867106e-02 1.52858620e-02 1.22689277e-02
 1.04240755e-02 7.62705251e-03 7.06604378e-03 4.33510917e-03
 3.60536462e-03 2.94853446e-03 2.03049386e-03 1.65526415e-03
 1.47586254e-03 1.23315295e-03 7.59079984e-04 7.36798139e-04
 5.73351781e-04 4.35872127e-04 3.99872030e-04 3.42361042e-04
 2.62730088e-04 1.95627392e-04 1.82355600e-04 1.68717809e-04
 9.63213881e-05]

plt.figure(figsize=(10, 5))
plt.plot(range(1, len(pca.explained_variance_ratio_) + 1), pca.explained_variance_ratio_, marker='o')
plt.xlabel('Principal Component')
plt.ylabel('Proportion of Variance Explained')
plt.title('Proportion of Variance Explained by Principal Components')
plt.grid(True)
plt.show()

cumulative_explained_variance = np.cumsum(pca.explained_variance_ratio_)
plt.figure(figsize=(10, 5))
plt.plot(range(1, len(cumulative_explained_variance) + 1), cumulative_explained_variance, marker='o')
plt.xlabel('Number of Principal Components')
plt.ylabel('Cumulative Proportion of Variance Explained')
plt.title('Cumulative Proportion of Variance Explained by Principal Components')
plt.grid(True)
plt.show()

pca = PCA(n_components=7)
pca.fit(df_scaled)
components_df = pd.DataFrame(pca.components_.T, index=df.columns.drop(['Entity', 'Code', 'Year']), columns=[f'PC{i+1}' for i in range(pca.n_components_)])
print(components_df)
pca_out = pca.transform(df_scaled)

                            PC1       PC2       PC3       PC4       PC5  \
INTS_Deaths            0.094227 -0.279744  0.463189 -0.097988 -0.311449   
Violence_Deaths        0.192083  0.222668  0.146339 -0.055789 -0.035394   
Nutrition_Deaths       0.185846 -0.098552 -0.229165  0.030813  0.090948   
Hepatitis_Deaths       0.181262 -0.071792 -0.355795 -0.059059 -0.015808   
Neoplasms_Deaths       0.189168  0.308176  0.059227 -0.001661  0.086901   
Measles_Deaths         0.190922 -0.195315 -0.058128  0.001188 -0.004194   
Digestive_Deaths       0.204316  0.154755  0.155294 -0.032092 -0.091682   
Cirrhosis_Deaths       0.207761 -0.142255 -0.120225 -0.020686 -0.116311   
Kidney_Deaths          0.203901  0.060996  0.144088  0.068227 -0.049469   
Cardiovascular_Deaths  0.153002  0.310854  0.170639 -0.037460 -0.036617   
Congenital_Deaths      0.210769  0.182265  0.024624 -0.018932 -0.016313   
Respiratory_Deaths     0.221859  0.003205 -0.048193 -0.041515 -0.054279   
Preterm_Deaths         0.212775 -0.004188 -0.175389 -0.018307 -0.026728   
Heat_Cold_Deaths       0.201447  0.150567 -0.138287 -0.088340 -0.017715   
Sepsis_Deaths          0.204937 -0.181966 -0.100790  0.021038  0.033891   
Nature_Deaths          0.021530  0.025108 -0.033360  0.933804 -0.320722   
Diabetes_Deaths        0.151425  0.185858  0.211426  0.150184  0.291552   
Encephalopathy_Deaths  0.205653 -0.058167 -0.058967  0.010093  0.001525   
Meningitis_Deaths      0.200081 -0.191567  0.151517 -0.053601 -0.146360   
Other_Neonatal_Deaths  0.186166 -0.095823 -0.326120 -0.005729  0.037272   
Whooping_Cough_Deaths  0.216636 -0.095771 -0.121720 -0.004722  0.002849   
Diarrheal_Deaths       0.204768 -0.223387  0.033048 -0.042289 -0.123416   
Fire_Heat_Deaths       0.218313  0.060347  0.051362 -0.028369 -0.029486   
Road_Deaths            0.182034  0.251374  0.167682 -0.015125 -0.015366   
Tuberculosis_Deaths    0.205832 -0.118289 -0.014162  0.037863  0.044409   
HIV_AIDS_Deaths        0.067126 -0.195497  0.223282  0.218046  0.743423   
Drowning_Deaths        0.178385  0.319090 -0.047663  0.013700  0.006005   
Malaria_Deaths         0.129861 -0.318591  0.386044 -0.031780 -0.070264   
Syphilis_Deaths        0.193933 -0.157282 -0.001519  0.118707  0.260574   

                            PC6       PC7  
INTS_Deaths           -0.150491 -0.160460  
Violence_Deaths       -0.211198 -0.073727  
Nutrition_Deaths       0.162417  0.419810  
Hepatitis_Deaths      -0.181882 -0.073030  
Neoplasms_Deaths      -0.085367  0.004181  
Measles_Deaths         0.131766  0.354851  
Digestive_Deaths      -0.102466 -0.013309  
Cirrhosis_Deaths      -0.004083 -0.084571  
Kidney_Deaths          0.238568 -0.130608  
Cardiovascular_Deaths -0.069920  0.144762  
Congenital_Deaths     -0.090481 -0.163793  
Respiratory_Deaths    -0.076192  0.086761  
Preterm_Deaths        -0.084886 -0.221464  
Heat_Cold_Deaths      -0.275361  0.125037  
Sepsis_Deaths          0.034137 -0.287823  
Nature_Deaths         -0.124072  0.033323  
Diabetes_Deaths        0.524986 -0.306077  
Encephalopathy_Deaths  0.027057 -0.265309  
Meningitis_Deaths     -0.045498 -0.004897  
Other_Neonatal_Deaths -0.070486 -0.265279  
Whooping_Cough_Deaths -0.000840  0.023613  
Diarrheal_Deaths      -0.003502  0.130329  
Fire_Heat_Deaths      -0.076153  0.056770  
Road_Deaths            0.066890  0.188621  
Tuberculosis_Deaths    0.300646  0.298881  
HIV_AIDS_Deaths       -0.472547  0.074565  
Drowning_Deaths        0.021078  0.156610  
Malaria_Deaths         0.005307  0.110629  
Syphilis_Deaths        0.234327 -0.102497  

plt.figure(figsize=(14, 10))

for i in range(6): 
    plt.scatter(pca_out[:, i], pca_out[:, i + 1], label=f'PC{i+1} vs PC{i+2}', s=5)

plt.xlabel('Principal Component')
plt.ylabel('Principal Component')
plt.title('Scatter Plot of the First Seven Principal Components')
plt.legend()
plt.show()

SVD

U, s, V = np.linalg.svd(df_scaled, full_matrices=False)

np.round(V.T, 3)

array([[ 0.094,  0.28 , -0.463,  0.098, -0.311,  0.15 , -0.16 ,  0.169,
        -0.019,  0.069, -0.101, -0.211,  0.041,  0.095,  0.002, -0.224,
        -0.349,  0.434,  0.163,  0.097,  0.001,  0.075,  0.095, -0.098,
        -0.088, -0.045, -0.002, -0.108, -0.046],
       [ 0.192, -0.223, -0.146,  0.056, -0.035,  0.211, -0.074,  0.163,
         0.252,  0.142,  0.175,  0.048,  0.298,  0.036, -0.169, -0.378,
         0.593,  0.092,  0.103, -0.174, -0.034,  0.051, -0.083,  0.103,
         0.058, -0.05 ,  0.057,  0.022,  0.029],
       [ 0.186,  0.099,  0.229, -0.031,  0.091, -0.162,  0.42 ,  0.2  ,
         0.001,  0.167, -0.187, -0.241,  0.592, -0.232, -0.066,  0.116,
        -0.133,  0.08 ,  0.055, -0.127, -0.044, -0.009, -0.094, -0.186,
         0.061,  0.032, -0.039, -0.112, -0.03 ],
       [ 0.181,  0.072,  0.356,  0.059, -0.016,  0.182, -0.073,  0.11 ,
        -0.226,  0.131,  0.092,  0.08 , -0.225,  0.034,  0.019, -0.044,
        -0.038,  0.085,  0.091, -0.237, -0.216,  0.078, -0.303,  0.178,
        -0.4  ,  0.138, -0.28 , -0.367,  0.055],
       [ 0.189, -0.308, -0.059,  0.002,  0.087,  0.085,  0.004, -0.014,
         0.241, -0.111,  0.198, -0.09 ,  0.089,  0.04 ,  0.02 ,  0.39 ,
        -0.174,  0.079, -0.068, -0.093,  0.08 ,  0.037,  0.207,  0.143,
        -0.389, -0.351,  0.316, -0.234,  0.146],
       [ 0.191,  0.195,  0.058, -0.001, -0.004, -0.132,  0.355, -0.324,
         0.327,  0.191,  0.114,  0.356, -0.248,  0.188, -0.295,  0.098,
        -0.022,  0.231,  0.272,  0.053, -0.116,  0.123,  0.016, -0.142,
         0.087, -0.055,  0.052,  0.008, -0.067],
       [ 0.204, -0.155, -0.155,  0.032, -0.092,  0.102, -0.013,  0.087,
         0.031, -0.25 , -0.181,  0.234,  0.07 ,  0.229,  0.338,  0.249,
         0.098, -0.091,  0.028, -0.055, -0.379, -0.051,  0.15 , -0.22 ,
         0.307, -0.024, -0.262, -0.293, -0.095],
       [ 0.208,  0.142,  0.12 ,  0.021, -0.116,  0.004, -0.085,  0.036,
        -0.175, -0.093, -0.353,  0.221,  0.12 ,  0.015,  0.054, -0.016,
        -0.04 , -0.082,  0.05 , -0.092, -0.358,  0.196,  0.003,  0.24 ,
        -0.148, -0.235,  0.327,  0.486, -0.075],
       [ 0.204, -0.061, -0.144, -0.068, -0.049, -0.239, -0.131, -0.175,
         0.109, -0.116, -0.498,  0.007, -0.13 , -0.074, -0.144, -0.133,
         0.078, -0.278,  0.13 , -0.321,  0.344,  0.128,  0.013, -0.2  ,
        -0.228,  0.124,  0.026, -0.201, -0.041],
       [ 0.153, -0.311, -0.171,  0.037, -0.037,  0.07 ,  0.145, -0.568,
        -0.467,  0.356, -0.084, -0.287,  0.042,  0.126,  0.047,  0.018,
         0.094,  0.004,  0.063,  0.055, -0.101, -0.098,  0.05 ,  0.074,
        -0.003,  0.017,  0.019,  0.005, -0.028],
       [ 0.211, -0.182, -0.025,  0.019, -0.016,  0.09 , -0.164,  0.012,
        -0.023, -0.044,  0.002,  0.185,  0.062, -0.152, -0.348,  0.228,
        -0.129,  0.251, -0.128, -0.167,  0.105, -0.303,  0.229,  0.096,
        -0.059,  0.271, -0.333,  0.332, -0.251],
       [ 0.222, -0.003,  0.048,  0.042, -0.054,  0.076,  0.087,  0.051,
         0.051,  0.124, -0.053, -0.044, -0.138,  0.042,  0.152, -0.023,
        -0.017,  0.014, -0.123, -0.111,  0.086,  0.053,  0.145, -0.227,
         0.009, -0.014, -0.242,  0.349,  0.754],
       [ 0.213,  0.004,  0.175,  0.018, -0.027,  0.085, -0.221, -0.011,
        -0.093, -0.045, -0.005,  0.081, -0.052, -0.175, -0.11 , -0.038,
        -0.032,  0.092, -0.024,  0.109, -0.138, -0.254,  0.071, -0.148,
         0.22 ,  0.408,  0.586, -0.214,  0.257],
       [ 0.201, -0.151,  0.138,  0.088, -0.018,  0.275,  0.125,  0.239,
        -0.012,  0.136,  0.209, -0.082, -0.139,  0.169,  0.227, -0.016,
        -0.089, -0.229,  0.069,  0.035,  0.207,  0.148,  0.03 , -0.385,
        -0.106,  0.203,  0.198,  0.237, -0.404],
       [ 0.205,  0.182,  0.101, -0.021,  0.034, -0.034, -0.288,  0.005,
        -0.011,  0.111, -0.103,  0.094,  0.205,  0.031,  0.066,  0.277,
         0.31 , -0.008,  0.16 ,  0.647,  0.23 ,  0.002, -0.026, -0.036,
        -0.236,  0.   , -0.139, -0.012,  0.045],
       [ 0.022, -0.025,  0.033, -0.934, -0.321,  0.124,  0.033,  0.018,
        -0.017,  0.03 ,  0.068, -0.001,  0.003,  0.003,  0.008,  0.007,
         0.001,  0.002,  0.001,  0.002, -0.002, -0.001, -0.003, -0.003,
        -0.004, -0.003, -0.001,  0.004, -0.   ],
       [ 0.151, -0.186, -0.211, -0.15 ,  0.292, -0.525, -0.306,  0.213,
        -0.026,  0.468,  0.123,  0.166, -0.075, -0.054,  0.196, -0.039,
        -0.166,  0.035, -0.055, -0.043, -0.117,  0.058, -0.067, -0.01 ,
         0.078, -0.03 ,  0.023,  0.018, -0.04 ],
       [ 0.206,  0.058,  0.059, -0.01 ,  0.002, -0.027, -0.265, -0.345,
         0.302, -0.19 ,  0.221, -0.387,  0.035, -0.277,  0.139,  0.052,
        -0.053, -0.012,  0.033,  0.009, -0.222,  0.415, -0.057,  0.067,
         0.132,  0.218, -0.137,  0.112, -0.068],
       [ 0.2  ,  0.192, -0.152,  0.054, -0.146,  0.045, -0.005, -0.198,
         0.229,  0.005,  0.087,  0.047,  0.034, -0.07 ,  0.285,  0.027,
        -0.092, -0.12 , -0.05 , -0.115,  0.093, -0.559, -0.542, -0.002,
        -0.03 , -0.122,  0.048,  0.108, -0.036],
       [ 0.186,  0.096,  0.326,  0.006,  0.037,  0.07 , -0.265, -0.06 ,
        -0.176,  0.013,  0.068, -0.127, -0.155, -0.189, -0.11 , -0.122,
         0.044,  0.046, -0.005, -0.086,  0.119, -0.098,  0.149, -0.244,
         0.266, -0.635, -0.081, -0.079, -0.165],
       [ 0.217,  0.096,  0.122,  0.005,  0.003,  0.001,  0.024,  0.063,
        -0.04 ,  0.011, -0.015,  0.01 ,  0.025,  0.24 ,  0.17 ,  0.033,
        -0.133, -0.022,  0.234, -0.121,  0.457,  0.06 ,  0.088,  0.565,
         0.44 ,  0.085,  0.023, -0.074,  0.032],
       [ 0.205,  0.223, -0.033,  0.042, -0.123,  0.004,  0.13 , -0.078,
         0.001,  0.122, -0.045,  0.106, -0.004,  0.025,  0.051, -0.047,
         0.128,  0.072, -0.825,  0.048,  0.118,  0.224,  0.04 ,  0.085,
         0.023,  0.035,  0.047, -0.172, -0.151],
       [ 0.218, -0.06 , -0.051,  0.028, -0.029,  0.076,  0.057,  0.008,
         0.077,  0.033,  0.111,  0.094,  0.125, -0.008, -0.314, -0.326,
        -0.379, -0.577, -0.048,  0.321, -0.139, -0.057,  0.111,  0.132,
        -0.004, -0.066, -0.176, -0.107,  0.072],
       [ 0.182, -0.251, -0.168,  0.015, -0.015, -0.067,  0.189, -0.021,
        -0.383, -0.417,  0.194,  0.287,  0.052, -0.233, -0.011, -0.081,
        -0.078,  0.18 ,  0.046,  0.151,  0.216,  0.267, -0.348, -0.101,
         0.066, -0.072, -0.019,  0.017,  0.088],
       [ 0.206,  0.118,  0.014, -0.038,  0.044, -0.301,  0.299,  0.042,
        -0.028, -0.235,  0.15 , -0.06 , -0.135, -0.184,  0.319, -0.323,
         0.197,  0.092,  0.102,  0.081, -0.07 , -0.275,  0.422,  0.136,
        -0.253,  0.058, -0.034, -0.002, -0.091],
       [ 0.067,  0.195, -0.223, -0.218,  0.743,  0.473,  0.075, -0.07 ,
         0.014, -0.02 , -0.186,  0.068, -0.052, -0.122,  0.071, -0.082,
        -0.038,  0.044,  0.006,  0.004, -0.002,  0.019, -0.   ,  0.015,
        -0.002,  0.012, -0.   ,  0.003, -0.015],
       [ 0.178, -0.319,  0.048, -0.014,  0.006, -0.021,  0.157,  0.274,
         0.205, -0.06 , -0.372, -0.329, -0.427, -0.02 , -0.13 ,  0.023,
         0.043,  0.142, -0.076,  0.311, -0.092, -0.08 , -0.27 ,  0.183,
         0.119, -0.073, -0.012,  0.047, -0.063],
       [ 0.13 ,  0.319, -0.386,  0.032, -0.07 , -0.005,  0.111,  0.258,
        -0.23 ,  0.052,  0.204, -0.155, -0.227, -0.227, -0.245,  0.411,
         0.244, -0.304,  0.081, -0.12 , -0.071,  0.04 ,  0.015,  0.07 ,
         0.074, -0.002,  0.052,  0.005,  0.041],
       [ 0.194,  0.157,  0.002, -0.119,  0.261, -0.234, -0.102,  0.021,
        -0.145, -0.333,  0.155, -0.267,  0.091,  0.637, -0.249, -0.046,
         0.032,  0.022, -0.135, -0.062, -0.063, -0.101, -0.137, -0.122,
        -0.042,  0.048,  0.013,  0.092,  0.029]])

pca_out = pca.transform(df_scaled)
pca_out

array([[ 1.05030157,  0.32806136, -0.26212573, ..., -0.27747282,
         0.35391138,  0.01280102],
       [ 1.13209969,  0.36087074, -0.26075422, ..., -0.38430763,
         0.31305797, -0.04196272],
       [ 1.3725952 ,  0.42210871, -0.23694544, ..., -0.32019865,
         0.36630812, -0.15413755],
       ...,
       [-0.49005491, -0.1667606 , -0.07166429, ...,  0.36594767,
        -0.11585224,  0.0633935 ],
       [-0.51580557, -0.15763107, -0.09296948, ...,  0.32501016,
        -0.08830259,  0.05705652],
       [-0.52409014, -0.14791434, -0.10220481, ...,  0.25663525,
        -0.09838061,  0.05373432]])

def fit_svd(X, M=1):
    U, s, V = np.linalg.svd(X, full_matrices=False)
    return U[:, :M] @ (np.diag(s[:M]) @ V[:M, :]) 

df_imputed = df_scaled.copy()

row_index = np.random.choice(len(df_imputed), size=20, replace=False)
column_index = np.random.choice(df_imputed.shape[1], size=20)

Xhat = df_imputed.copy()
xbar = np.nanmean(df_imputed, axis=0)
Xhat[row_index, column_index] = xbar[column_index]

thresh = 1e-7
rel_err = 1
iter_ = 0

ismiss = np.isnan(df_imputed)
Xscaled = (df_imputed - xbar) / np.sqrt(np.sum(~ismiss, axis=0))
Xscaled_nomiss = Xscaled[~ismiss]
mssold = np.mean(np.square(Xscaled_nomiss))
mss0 = np.mean(np.square(df_imputed[~ismiss]))

while rel_err > thresh:
    iter_ += 1
    Xapp = fit_svd(Xhat, M=1)
    Xhat[ismiss] = Xapp[ismiss]
    mss = np.mean(np.square(df_imputed[~ismiss] - Xapp[~ismiss]))
    rel_err = (mssold - mss) / mss0
    mssold = mss
    
    print(f"Iter: {iter_}, MSS: {mss}, Rel. Err: {rel_err}")

Iter: 1, MSS: 0.3201319244821912, Rel. Err: -0.31996852578938073

U, s, V = np.linalg.svd(df_scaled, full_matrices=False)

plt.figure(figsize=(10, 6))
plt.plot(range(1, len(s) + 1), s ** 2, marker='o', linestyle='-')
plt.title('Scree Plot')
plt.xlabel('Component Index')
plt.ylabel('Eigenvalue')
plt.grid(True)
plt.show()

cumulative_variance_explained = np.cumsum(s ** 2) / np.sum(s ** 2)
plt.figure(figsize=(10, 6))
plt.plot(range(1, len(s) + 1), cumulative_variance_explained, marker='o', linestyle='-')
plt.title('Cumulative Variance Explained Plot')
plt.xlabel('Number of Components')
plt.ylabel('Cumulative Proportion of Variance Explained')
plt.grid(True)
plt.show()

Clustering

K Means

inertia_values = []
k_values = range(2, 30)

for k in k_values:
    model = KMeans(n_clusters=k, random_state=42)
    model.fit(df_scaled)
    inertia_values.append(model.inertia_)
    
plt.figure(figsize=(10, 6))
plt.plot(k_values, inertia_values, marker='o')
plt.title('Elbow Method for Optimal k')
plt.xlabel('Number of Clusters (k)')
plt.ylabel('Inertia')
plt.xticks(k_values)
plt.grid(True)
plt.show()

kmeans_2 = KMeans(n_clusters=2, random_state=42)
kmeans_2.fit(df_scaled)
labels_2 = kmeans_2.labels_

fig, ax = plt.subplots(figsize=(8, 8))
ax.scatter(df_scaled[:, 0], df_scaled[:, 1], c=labels_2)
ax.set_title("K-Means Clustering Results with K=2")
plt.show()

kmeans_17 = KMeans(n_clusters=17, random_state=42)
kmeans_17.fit(df_scaled)
labels_17 = kmeans_17.labels_

fig, ax = plt.subplots(figsize=(8, 8))
ax.scatter(df_scaled[:, 0], df_scaled[:, 1], c=labels_17)
ax.set_title("K-Means Clustering Results with K=17")
plt.show()

kmeans_29 = KMeans(n_clusters=29, random_state=42)
kmeans_29.fit(df_scaled)
labels_29 = kmeans_29.labels_

fig, ax = plt.subplots(figsize=(8, 8))
ax.scatter(df_scaled[:, 0], df_scaled[:, 1], c=labels_29)
ax.set_title("K-Means Clustering Results with K=29")
plt.show()

kmeans_2_1 = KMeans(n_clusters=2, random_state=3, n_init=1)
kmeans_2_1.fit(df_scaled)
inertia_2_1 = kmeans_2_1.inertia_

kmeans_2_20 = KMeans(n_clusters=2, random_state=3, n_init=20)
kmeans_2_20.fit(df_scaled)
inertia_2_20 = kmeans_2_20.inertia_

print("Inertia for KMeans with 2 clusters and n_init=1:", inertia_2_1)
print("Inertia for KMeans with 2 clusters and n_init=20:", inertia_2_20)

kmeans_17_1 = KMeans(n_clusters=17, random_state=3, n_init=1)
kmeans_17_1.fit(df_scaled)
inertia_17_1 = kmeans_17_1.inertia_

kmeans_17_20 = KMeans(n_clusters=17, random_state=3, n_init=20)
kmeans_17_20.fit(df_scaled)
inertia_17_20 = kmeans_17_20.inertia_

print("Inertia for KMeans with 17 clusters and n_init=1:", inertia_17_1)
print("Inertia for KMeans with 17 clusters and n_init=20:", inertia_17_20)

kmeans_29_1 = KMeans(n_clusters=29, random_state=3, n_init=1)
kmeans_29_1.fit(df_scaled)
inertia_29_1 = kmeans_29_1.inertia_

kmeans_29_20 = KMeans(n_clusters=29, random_state=3, n_init=20)
kmeans_29_20.fit(df_scaled)
inertia_29_20 = kmeans_29_20.inertia_

print("Inertia for KMeans with 29 clusters and n_init=1:", inertia_29_1)
print("Inertia for KMeans with 29 clusters and n_init=20:", inertia_29_20)

Inertia for KMeans with 2 clusters and n_init=1: 92144.82758027197
Inertia for KMeans with 2 clusters and n_init=20: 92144.82758027197
Inertia for KMeans with 17 clusters and n_init=1: 15774.632372538665
Inertia for KMeans with 17 clusters and n_init=20: 14187.884503011679
Inertia for KMeans with 29 clusters and n_init=1: 7970.138918026145
Inertia for KMeans with 29 clusters and n_init=20: 7837.581214293692

Hierarchical Clustering

numeric_df = df.drop(columns=['Entity', 'Code', 'Year'])

linkage_methods = ['single', 'complete', 'average']

def plot_dendrograms_sklearn(data, axes, title):
    for method, ax in zip(linkage_methods, axes.flatten()[:-1]):  
        hc = AgglomerativeClustering(distance_threshold=0, n_clusters=None, linkage=method)
        hc.fit(data)
        Z = linkage(data, method=method)
        dendrogram(Z, ax=ax, truncate_mode='level', p=5, color_threshold=5, above_threshold_color='black')
        ax.set_title(f'Linkage Method: {method.capitalize()}')
    plt.suptitle(title, fontsize=20)
    plt.tight_layout(rect=[0, 0, 1, 0.96])

def plot_dendrogram_centroid(data, ax):
    Z = linkage(data, method='centroid')
    dendrogram(Z, ax=ax, truncate_mode='level', p=5, color_threshold=5, above_threshold_color='black')
    ax.set_title('Linkage Method: Centroid')

fig, axes = plt.subplots(2, 2, figsize=(20, 15))
plot_dendrograms_sklearn(numeric_df, axes, "Hierarchical Clustering with Original Data")
plot_dendrogram_centroid(numeric_df, axes.flatten()[-1])
plt.show()

fig, axes = plt.subplots(2, 2, figsize=(20, 15))
plot_dendrograms_sklearn(df_scaled, axes, "Hierarchical Clustering with Scaled Data")
plot_dendrogram_centroid(df_scaled, axes.flatten()[-1])
plt.show()

def cut_and_print_clusters(data, Z, n_clusters, entity_column='Entity'):
    clusters = cut_tree(Z, n_clusters=n_clusters).flatten()
    data['Cluster'] = clusters
    for cluster in range(n_clusters):
        print(f"\nCluster {cluster + 1}:")
        print(data[data['Cluster'] == cluster][['Cluster', entity_column]])

print("Original Data Clusters:")
Z_complete_original = linkage(numeric_df, method='complete')
cut_and_print_clusters(df.copy(), Z_complete_original, n_clusters=5
                      )

print("\nScaled Data Clusters:")
Z_complete_scaled = linkage(df_scaled, method='complete')
scaled_df_with_clusters = pd.DataFrame(df_scaled, columns=numeric_df.columns)
scaled_df_with_clusters['Entity'] = df['Entity'].values  
cut_and_print_clusters(scaled_df_with_clusters, Z_complete_scaled, n_clusters=5, entity_column='Entity')

Original Data Clusters:

Cluster 1:
      Cluster       Entity
0           0  Afghanistan
1           0  Afghanistan
2           0  Afghanistan
3           0  Afghanistan
4           0  Afghanistan
...       ...          ...
6835        0     Zimbabwe
6836        0     Zimbabwe
6837        0     Zimbabwe
6838        0     Zimbabwe
6839        0     Zimbabwe

[6050 rows x 2 columns]

Cluster 2:
      Cluster Entity
1140        1  China
1141        1  China
1142        1  China
1143        1  China
1144        1  China
1145        1  China
1146        1  China
1147        1  China
1148        1  China
1149        1  China
2692        1  India
2693        1  India
2694        1  India
2695        1  India
2696        1  India
2697        1  India
2698        1  India
2699        1  India

Cluster 3:
      Cluster Entity
2670        2  India
2671        2  India
2672        2  India
2673        2  India
2674        2  India
2675        2  India
2676        2  India
2677        2  India
2678        2  India
2679        2  India
2680        2  India
2681        2  India
2682        2  India

Cluster 4:
      Cluster Entity
2683        3  India
2684        3  India
2685        3  India
2686        3  India
2687        3  India
2688        3  India
2689        3  India
2690        3  India
2691        3  India

Cluster 5:
      Cluster   Entity
4170        4  Nigeria
4171        4  Nigeria
4172        4  Nigeria
4173        4  Nigeria
4174        4  Nigeria
4175        4  Nigeria
4176        4  Nigeria
4177        4  Nigeria
4178        4  Nigeria
4179        4  Nigeria
4180        4  Nigeria
4181        4  Nigeria
4182        4  Nigeria
4183        4  Nigeria
4184        4  Nigeria
4185        4  Nigeria
4186        4  Nigeria
4187        4  Nigeria
4188        4  Nigeria
4189        4  Nigeria
4190        4  Nigeria
4191        4  Nigeria
4192        4  Nigeria
4193        4  Nigeria
4194        4  Nigeria
4195        4  Nigeria
4196        4  Nigeria
4197        4  Nigeria
4198        4  Nigeria
4199        4  Nigeria

Scaled Data Clusters:

Cluster 1:
      Cluster       Entity
0           0  Afghanistan
1           0  Afghanistan
2           0  Afghanistan
3           0  Afghanistan
4           0  Afghanistan
...       ...          ...
6115        0     Zimbabwe
6116        0     Zimbabwe
6117        0     Zimbabwe
6118        0     Zimbabwe
6119        0     Zimbabwe

[6082 rows x 2 columns]

Cluster 2:
      Cluster      Entity
421         1  Bangladesh
2360        1       Haiti
2504        1   Indonesia
2520        1        Iran
3678        1     Myanmar
4125        1    Pakistan

Cluster 3:
      Cluster Entity
1110        2  China
1111        2  China
1112        2  China
1113        2  China
1114        2  China
1115        2  China
1116        2  China
1117        2  China
1118        2  China
1119        2  China
1120        2  China

Cluster 4:
      Cluster Entity
2460        3  India
2461        3  India
2462        3  India
2463        3  India
2464        3  India
2465        3  India
2466        3  India
2467        3  India
2468        3  India
2469        3  India

Cluster 5:
      Cluster Entity
2470        4  India
2471        4  India
2472        4  India
2473        4  India
2474        4  India
2475        4  India
2476        4  India
2477        4  India
2478        4  India
2479        4  India
2480        4  India